Systems | Development | Analytics | API | Testing

A very common request we get from analytics users across all industries is the ability to export dashboard and analytic content to external files for collaboration and sharing. Whether the requirement is to export to PDF or export to XLS, exporting dashboard content to another popular media format remains an expected and important feature of modern business intelligence (BI). However, many analytics vendors lack options in exporting content, limiting the ability to share reports with non-analytics users.

By now, every organization, regardless of industry, has at least explored the use of AI, if not already embraced it. In today’s market, the AI imperative is firmly here, and failing to act quickly could mean getting left behind. But even as adoption soars, struggles remain, and scalability continues to be a major issue. Organizations are quick to adopt AI, but getting it established across the organization brings a unique set of challenges that come into play.

Digitalization, as we’re talking about in this article, refers to the strategic use of digital technologies and analytics to shift customer interactions from physical locations to digital platforms like apps and online services.

At Snowflake, our product philosophy is centered on a single unified platform, and thus, a single core engine. To support all your workload needs with unmatched performance and efficiency, we continuously enhance and deliver performance improvements on this core engine. This approach allows you to benefit from enhanced performance effortlessly, without incurring additional costs or requiring migrations, upgrades or manual intervention.

This summer marks five years since we announced our first fully managed connector on Confluent Cloud in 2019, the Amazon S3 Sink Connector. Since then, our connector offerings have not only expanded significantly but also enabled teams to send hundreds of petabytes of data throughput. Today, we support over 80 pre-built, fully managed connectors, custom connectors, and secure private networking.

Hey, you! Yeah, you! The puzzled-looking Spring Boot developer, scouring the web for a guide on integrating your microservices with Apache Kafka on Confluent Cloud with Stream Governance. Admit it, you’ve been Googling nonstop for the past hour and all you’ve found are examples using StringSerializer/StringDeserializer with not even the slightest mention of "schema registry-aware" serialization methods. And I bet the examples you found are implemented in Java.

Organizations today are accumulating data more than ever. Traditional data management approaches, such as centralized data warehouses and siloed data marts, are struggling to keep pace with the ever-increasing volume, velocity, and variety of information. The complexity of modern data environments is outpacing the capabilities of these legacy systems and demands a more agile and distributed solution.